NCT05838456

Deep Learning Enabled Endovascular Stroke Therapy Screening in Community Hospitals

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 443 (actual)
• Sponsor: The University of Texas Health Science Center, Houston · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

After onset of Acute Ischemic Stroke (AIS), every minute of delay to treatment reduces the likelihood of a good clinical outcome. A key delay occurs in the time between completion of computed tomography (CT) angiography of the head and neck and interpretation in the setting of AIS care. The purpose of this study is to assess the effect of incorporating Viz.AI software, which via via a machine-learning algorithm performs artificial intelligence-based automated detection of large vessel occlusions (LVO) on CT angiography (CTA) images and alerts the AIS care team (diagnosis and treatment decisions will be based on the clinical evaluation and review of the images by the treating physician, per routine standard of care). The hypothesis is that integration of the software into the AIS care pathway will reduce delays in treatment. A cluster-randomized stepped-wedge trial will be performed across 4 hospitals in the greater Houston area.

Conditions

• Acute Ischemic Stroke (AIS)

Interventions

Type	Name	Description
DEVICE	Viz.AI software	Viz.AI software performs artificial intelligence-based automated detection of large vessel occlusions and alerts the AIS care team.

Timeline

• Start date: 2021-01-01
• Primary completion: 2022-02-28
• Completion: 2022-05-27
• First posted: 2023-05-01
• Last updated: 2023-06-28
• Results posted: 2023-06-28

Locations

1 site across 1 country: United States

Regulatory

• FDA-regulated device study